Think with Structured Grounding: Perceptual Reinforcement Learning for Chart and Visual-Tabular Understanding
Multimodal Large Language Models (MLLMs) capable of thinking with images often rely on external tools for fine-grained perception. However, this reliance introduces significant inference latency and fails to effectively resolve the spatial-structural gap-a fundamental challenge in text-dense and structurally relational visuals (e.g., charts and visual tables) where strict relative spatial arrangements bind textual elements. Without external tools, standard MLLMs struggle with such fine-grained visual reasoning tasks. To address these issues, we propose Think with Structured Grounding (TwSG), a novel fine-grained image perception framework designed to internalize complex images's tool-use ...
| Authors | Changjiang Jiang, Qiannian Zhao, Lei Xin et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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